Browsing by Author "Adali, Sumeyye"

Now showing 1 - 2 of 2

Genetic Relationships of Salep Orchid Species and Gene Flow Among Serapias Vomeracea X Anacamptis Morio Hybrids
(Springer, 2023) Palaz, Esra Bulunuz; Demirel, Fatih; Adali, Sumeyye; Demirel, Serap; Yilmaz, Abdurrahim
Orchids are naturally grown in many countries of the Eastern Mediterranean. Salep, produced from orchid tubers via grinding and used as a hot drink, is an essential ingredient of ice cream. Salep orchid species are in danger of extinction due to the absence of cultivation and over-harvesting from nature. In this study, the genetic diversity and population structure between salep orchid species, their hybrids, and commercial species of Phalenopsis sp. were first investigated using inter-Primer Binding Site (iPBS) Retrotransposon markers. A total of 854 bands were scored with a 100% polymorphism rate. Neighbor-joining, model-based structure, and PCoA (Principal Coordinate Analysis) algorithms clustered the 30 salep orchids into three main populations. The analysis of molecular variance revealed variations within and among the populations as 71% and 29%, respectively. Anacamptis morio and Serapias vomeracea had the furthest genetic distances, and F-1 hybrids of S. vomeracea and A. morio had great genetic diversity. The study results will provide helpful information for orchid breeding by eliciting the genetic distances of salep orchids.
Integrating in Vitro Propagation and Machine Learning Modeling for Efficient Shoot and Root Development in Aronia Melanocarpa
(MDPI, 2025) Yaman, Mehmet; Palaz, Esra Bulunuz; Isak, Musab A.; Demirel, Serap; Izgu, Tolga; Adali, Sumeyye; Popescu, Monica
Aronia melanocarpa (black chokeberry) is a medicinally valuable small fruit species, yet its commercial propagation remains limited by low rooting and genotype-specific responses. This study developed an efficient, callus-free micropropagation and rooting protocol using a Shrub Plant Medium (SPM) supplemented with 5 mg/L BAP in large 660 mL jars, which yielded up to 27 shoots per explant. Optimal rooting (100%) was achieved with 0.5 mg/L NAA + 0.25 mg/L IBA in half-strength SPM. In the second phase, supervised machine learning models, including Random Forest (RF), XGBoost, Gaussian Process (GP), and Multilayer Perceptron (MLP), were employed to predict morphogenic traits based on culture conditions. XGBoost and RF outperformed other models, achieving R2 values exceeding 0.95 for key variables such as shoot number and root length. These results demonstrate that data-driven modeling can enhance protocol precision and reduce experimental workload in plant tissue culture. The study also highlights the potential for combining physiological understanding with artificial intelligence to streamline future in vitro applications in woody species.